The United States is one of the largest energy consumers per capita, requiring households to have adequate energy expenditures to keep up with modern demand regardless of financial cost. This paper investigates energy burden, defined as the ratio of household energy expenditures to household income.

There is a lack of research on creating equitable policies for energy-burdened communities, including environmental justice indicators and community characteristics that could be used to predict and understand energy burden, along with socioeconomic status, building characteristics, and power outages, beneficial to policymakers, engineers, and advocates. Here, generalized additive models and random forests are explored for energy burden prediction using the original dataset and principal components, followed by a leave-one-column-out (LOCO) analysis to investigate indicator influence, with 25 identical indicators out of 42 appearing in the top 100 models. The generalized additive models generally outperform the random forests, with the best-performing model yielding a coefficient of determination of 0.92.